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Optimal matrix rigidity for stress-fibre polarization in stem cells

Open access
Zemel, A., Rehfeldt, F., Brown, A. E. X., Discher, D. E., Safran, S. A.·2010·Nature Physics

Abstract

The shape and differentiation of human mesenchymal stem cells is especially sensitive to the rigidity of their environment; the physical mechanisms involved are unknown. A theoretical model and experiments demonstrate here that the polarization/alignment of stress-fibers within stem cells is a non-monotonic function of matrix rigidity. We treat the cell as an active elastic inclusion in a surrounding matrix whose polarizability, unlike dead matter, depends on the feedback of cellular forces that develop in response to matrix stresses. The theory correctly predicts the monotonic increase of the cellular forces with the matrix rigidity and the alignment of stress-fibers parallel to the long axis of cells. We show that the anisotropy of this alignment depends non-monotonically on matrix rigidity and demonstrate it experimentally by quantifying the orientational distribution of stress-fibers in stem cells. These findings offer a first physical insight for the dependence of stem cell differentiation on tissue elasticity.

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Metadata

Authors
Zemel, A., Rehfeldt, F., Brown, A. E. X., Discher, D. E., & Safran, S. A.
Venue
Nature Physics
Published
2010-03-21
Volume
6
Issue
6
Pages
468-473
ISSN
  • 1745-2473 (ISSN-L)
  • 1745-2481
OpenAlex
W2025003977
Cited by
363
References
4
FWCI
11.39
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Optimal matrix rigidity for stress-fibre polarization in stem cells. https://app.cochat.ai/papers/10.1038/nphys1613
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Zemel, A., Rehfeldt, F., Brown, A. E. X., Discher, D. E., & Safran, S. A. (2010). Optimal matrix rigidity for stress-fibre polarization in stem cells. Nature Physics. https://doi.org/10.1038/nphys1613

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.